Towards Causality-Aware Modeling for Multimodal Brain-Muscle Interactions

TL;DR

This paper presents a causality-aware modeling framework combining Dynamic Bayesian Networks and Convergent Cross Mapping to analyze multimodal brain-muscle interactions, improving causal inference and uncertainty quantification in biomedical signals.

Contribution

It introduces a novel DBN-informed CCM approach that integrates geometric manifold reconstruction with probabilistic temporal modeling for multimodal biomedical data analysis.

Findings

Enhanced predictive consistency over baseline methods

Improved causal stability in brain-muscle interaction modeling

Revealed frequency-specific reorganization in dystonia patients

Abstract

Robust characterization of dynamic causal interactions in multivariate biomedical signals is essential for advancing computational and algorithmic methods in biomedical imaging. Conventional approaches, such as Dynamic Bayesian Networks (DBNs), often assume linear or simple statistical dependencies, while manifold based techniques like Convergent Cross Mapping (CCM) capture nonlinear, lagged interactions but lack probabilistic quantification and interventional modeling. We introduce a DBN informed CCM framework that integrates geometric manifold reconstruction with probabilistic temporal modeling. Applied to multimodal EEG-EMG recordings from dystonic and neurotypical children, the method quantifies uncertainty, supports interventional simulation, and reveals distinct frequency specific reorganization of corticomuscular pathways in dystonia. Experimental results show marked improvements in predictive consistency and causal stability as compared to baseline approaches, demonstrating the potential of causality aware multimodal modeling for developing quantitative biomarkers and guiding targeted neuromodulatory interventions.